Build an AI System to Predict and Reduce Claims Denials

Most independent practices rely on the claim "scrubbers" built into their EHR or practice management software like Athenahealth or Kareo. These tools are useful for catching basic formatting errors, like a missing digit in a patient ID. They operate on a fixed set of generic rules and cannot detect nuanced, payer-specific denial patterns that cause the majority of revenue loss.

Consider a 15-person orthopedic practice that sees a high denial rate from a major commercial payer for knee arthroscopy procedures. A biller gets a denial with the vague reason code "Information Submitted Does Not Support All Services Billed." The biller must then manually cross-reference the surgeon's notes, the 837 claim file, and a 200-page payer policy PDF. After 45 minutes, they find a new, obscure rule requiring a specific modifier if a preliminary X-ray was not performed within 30 days. This manual, reactive work is repeated daily and is impossible to scale.

The structural problem is that off-the-shelf software is built for broad compliance, not performance. An EHR's logic is designed to serve thousands of practices and cannot be tuned to the unique denial patterns your practice experiences with your specific payers. Clearinghouse services are data pipes, not analytical engines. They transmit claims but do not learn from the remittance advice that comes back. To solve this, you need a system trained on your own historical data.

The first step is a data audit, conducted under a Business Associate Agreement (BAA) to ensure HIPAA compliance. Syntora would analyze 12-24 months of your de-identified claims data (837 files) and remittance advice (835 files). This process identifies the most frequent and costly denial reasons, confirms data quality, and establishes a performance baseline for the predictive model.

The system would be a FastAPI service deployed on AWS Lambda for secure, serverless processing. When a new claim is generated, its key data points are sent as a JSON payload to the API. A gradient-boosted model trained on your historical data returns a denial risk score in under 500ms. If the risk is high, the system uses the Claude API to parse relevant sections of payer policy documents and suggests a precise correction for the biller to review.

The final deliverable is a secure API endpoint that integrates with your existing workflow, an audit trail of every prediction logged to a Supabase database, and the full Python source code. The system does not replace your billers. It gives them a tool to focus their expertise on the highest-risk claims, armed with specific, data-driven recommendations. You receive the complete source code, a maintenance runbook, and full ownership of the system.